Challenges in natural language processing and natural language understanding by considering both technical and natural domains

Pouya Ardehkhani, Amir Vahedi, Hossein Aghababa
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引用次数: 0

Abstract

As deep learning became more sophisticated, it significantly increased the use of AI in industry, academia, and other sectors. NLP is a part of the deep learning paradigm that offers different types of systems mainly related to human language understanding, meaning, and interpretations. Nowadays, NLP is used in several applications, including sentiment analysis, categorization of texts, translation, etc. Due to this new usage, new challenges occurred. This paper discusses the challenges of developing or creating an NLP model and the problems that will be occurred in NLU. Moreover, the paper illustrates issues in both technical and natural domains that should be considered upon deployment or creation of NLP models or NLU systems.
从技术和自然两方面考虑自然语言处理和理解的挑战
随着深度学习变得越来越复杂,它大大增加了人工智能在工业、学术界和其他领域的应用。NLP是深度学习范式的一部分,它提供了主要与人类语言理解、意义和解释相关的不同类型的系统。目前,自然语言处理已广泛应用于情感分析、文本分类、翻译等领域。由于这种新的用法,出现了新的挑战。本文讨论了开发或创建自然语言处理模型所面临的挑战以及在自然语言处理中将出现的问题。此外,本文说明了在部署或创建NLP模型或NLU系统时应该考虑的技术和自然领域的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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